Application of Optimization and Machine Learning for Sentiment Analysis

Authors

  • Manitosh Chourasiya
  • Prof. Devendra Singh Rathore

DOI:

https://doi.org/10.24113/ijoscience.v7i9.411

Keywords:

Lexicon, Aspect level, Sentiment Analysis, Machine Learning.

Abstract

Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm.

The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.

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Author Biographies

Manitosh Chourasiya

M Tech Scholar

Rabindranath Tagore University

Bhopal, Madhya Pradesh, India

Prof. Devendra Singh Rathore

Assistant Professor

Rabindranath Tagore University

Bhopal, Madhya Pradesh, India

References

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Published

10/25/2021

How to Cite

Chourasiya, M. ., & Rathore, P. . D. S. (2021). Application of Optimization and Machine Learning for Sentiment Analysis. SMART MOVES JOURNAL IJOSCIENCE, 7(9), 1–7. https://doi.org/10.24113/ijoscience.v7i9.411

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